Learning to recognize abnormalities in chest X-rays with location-aware dense networks

Gündel S, Grbic S, Georgescu B, Liu S, Maier A, Comaniciu D (2019)

Publication Type: Conference contribution

Publication year: 2019

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Verlag

Book Volume: 11401 LNCS

Pages Range: 757-765

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Madrid ES

ISBN: 9783030134686

DOI: 10.1007/978-3-030-13469-3_88

Abstract

Chest X-ray is the most common medical imaging exam used to assess multiple pathologies. Automated algorithms and tools have the potential to support the reading workflow, improve efficiency, and reduce reading errors. With the availability of large scale data sets, several methods have been proposed to classify pathologies on chest X-ray images. However, most methods report performance based on random image based splitting, ignoring the high probability of the same patient appearing in both training and test set. In addition, most methods fail to explicitly incorporate the spatial information of abnormalities or utilize the high resolution images. We propose a novel approach based on location aware Dense Networks (DNetLoc), whereby we incorporate both high-resolution image data and spatial information for abnormality classification. We evaluate our method on the largest data set reported in the community, containing a total of 86,876 patients and 297,541 chest X-ray images. We achieve (i) the best average AUC score for published training and test splits on the single benchmarking data set (ChestX-Ray14 [1]), and (ii) improved AUC scores when the pathology location information is explicitly used. To foster future research we demonstrate the limitations of the current benchmarking setup [1] and provide new reference patient-wise splits for the used data sets. This could support consistent and meaningful benchmarking of future methods on the largest publicly available data sets.

Authors with CRIS profile

Sebastian Gündel Lehrstuhl für Informatik 5 (Mustererkennung) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung)

Involved external institutions

Siemens AG, Healthcare Sector DE Germany (DE)

How to cite

APA:

Gündel, S., Grbic, S., Georgescu, B., Liu, S., Maier, A., & Comaniciu, D. (2019). Learning to recognize abnormalities in chest X-rays with location-aware dense networks. In Ruben Vera-Rodriguez, Julian Fierrez, Aythami Morales (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 757-765). Madrid, ES: Springer Verlag.

MLA:

Gündel, Sebastian, et al. "Learning to recognize abnormalities in chest X-rays with location-aware dense networks." Proceedings of the 23rd Iberoamerican Congress on Pattern Recognition, CIARP 2018, Madrid Ed. Ruben Vera-Rodriguez, Julian Fierrez, Aythami Morales, Springer Verlag, 2019. 757-765.

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